Alternatives to AWS Data Wrangler logo

Alternatives to AWS Data Wrangler

Pandas, NumPy, Anaconda, SciPy, and PySpark are the most popular alternatives and competitors to AWS Data Wrangler.
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What is AWS Data Wrangler and what are its top alternatives?

It is a utility belt to handle data on AWS. It aims to fill a gap between AWS Analytics Services (Glue, Athena, EMR, Redshift) and the most popular Python data libraries (Pandas, Apache Spark).
AWS Data Wrangler is a tool in the Data Science Tools category of a tech stack.
AWS Data Wrangler is an open source tool with 535 GitHub stars and 55 GitHub forks. Here鈥檚 a link to AWS Data Wrangler's open source repository on GitHub

Top Alternatives of AWS Data Wrangler

AWS Data Wrangler alternatives & related posts

Pandas logo

Pandas

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High-performance, easy-to-use data structures and data analysis tools for the Python programming language
Pandas logo
Pandas
VS
AWS Data Wrangler logo
AWS Data Wrangler

related Pandas posts

Guillaume Simler
Guillaume Simler
Jupyter
Jupyter
Anaconda
Anaconda
Pandas
Pandas
IPython
IPython

Jupyter Anaconda Pandas IPython

A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

See more
NumPy logo

NumPy

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Fundamental package for scientific computing with Python
NumPy logo
NumPy
VS
AWS Data Wrangler logo
AWS Data Wrangler
Anaconda logo

Anaconda

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The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
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    Anaconda logo
    Anaconda
    VS
    AWS Data Wrangler logo
    AWS Data Wrangler

    related Anaconda posts

    Guillaume Simler
    Guillaume Simler
    Jupyter
    Jupyter
    Anaconda
    Anaconda
    Pandas
    Pandas
    IPython
    IPython

    Jupyter Anaconda Pandas IPython

    A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

    The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

    See more
    SciPy logo

    SciPy

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    Scientific Computing Tools for Python
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      SciPy logo
      SciPy
      VS
      AWS Data Wrangler logo
      AWS Data Wrangler
      PySpark logo

      PySpark

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      The Python API for Spark
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        PySpark logo
        PySpark
        VS
        AWS Data Wrangler logo
        AWS Data Wrangler
        Pentaho Data Integration logo

        Pentaho Data Integration

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        Easy to Use With the Power to Integrate All Data Types
          Be the first to leave a pro
          Pentaho Data Integration logo
          Pentaho Data Integration
          VS
          AWS Data Wrangler logo
          AWS Data Wrangler
          Dask logo

          Dask

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          20
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          A flexible library for parallel computing in Python
            Be the first to leave a pro
            Dask logo
            Dask
            VS
            AWS Data Wrangler logo
            AWS Data Wrangler
            StreamSets logo

            StreamSets

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            14
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            Where DevOps Meets Data Integration
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              StreamSets logo
              StreamSets
              VS
              AWS Data Wrangler logo
              AWS Data Wrangler